{"id":9,"date":"2026-08-12T08:34:42","date_gmt":"2026-08-12T08:34:42","guid":{"rendered":"http:\/\/corpolearn.wp\/software\/\/mastering-dynamic-programming-graph-algorithms\/"},"modified":"2026-08-12T08:34:42","modified_gmt":"2026-08-12T08:34:42","slug":"mastering-dynamic-programming-graph-algorithms","status":"publish","type":"post","link":"https:\/\/corpolearn.com\/software\/mastering-dynamic-programming-graph-algorithms\/","title":{"rendered":"Mastering Dynamic Programming &#038; Graph Algorithms for Coding Interviews"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Conquering dynamic programming and graph algorithms requires recognizing underlying problem states and sub-problem optimal substructures.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Dijkstra Algorithm Template in Python<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code>import heapq\n\ndef shortest_path(graph, start_node, n):\n    distances = {i: float('inf') for i in range(1, n + 1)}\n    distances[start_node] = 0\n    pq = [(0, start_node)]\n    \n    while pq:\n        curr_dist, u = heapq.heappop(pq)\n        \n        if curr_dist &gt; distances[u]:\n            continue\n            \n        for v, weight in graph[u]:\n            if distances[u] + weight &lt; distances[v]:\n                distances[v] = distances[u] + weight\n                heapq.heappush(pq, (distances[v], v))\n                \n    return distances<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Tackle LeetCode Hard problems using memoization, bottom-up DP tables, Topological Sort, and Dijkstra algorithm templates.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_cl_workflow_state":"","_cl_content_brief":"","_cl_target_topic":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-9","post","type-post","status-publish","format-standard","hentry","category-dsa-algorithms"],"_links":{"self":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":0,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/posts\/9\/revisions"}],"wp:attachment":[{"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/media?parent=9"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/categories?post=9"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/corpolearn.com\/software\/wp-json\/wp\/v2\/tags?post=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}